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transformers\n","Successfully installed huggingface-hub-0.16.4 tokenizers-0.13.3 transformers-4.28.0\n","Collecting datasets\n","  Downloading datasets-2.13.1-py3-none-any.whl (486 kB)\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m486.2/486.2 kB\u001b[0m \u001b[31m8.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hRequirement already satisfied: numpy>=1.17 in /usr/local/lib/python3.10/dist-packages (from datasets) (1.22.4)\n","Requirement already satisfied: pyarrow>=8.0.0 in /usr/local/lib/python3.10/dist-packages (from datasets) (9.0.0)\n","Collecting dill<0.3.7,>=0.3.0 (from datasets)\n","  Downloading dill-0.3.6-py3-none-any.whl (110 kB)\n","\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m110.5/110.5 kB\u001b[0m \u001b[31m16.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25hRequirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from datasets) 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accelerate-0.21.0\n"]}],"source":["!pip install transformers==4.28.0\n","!pip install datasets\n","!pip install accelerate -U"]},{"cell_type":"code","source":["!gdown 1a1v06PvGaWmTCWvL33o2C133D90SRzMC # download cw_texts_clean_bert.csv\n","!mkdir output\n","!mkdir /data\n","!mv cw_texts_clean_bert.csv /data"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"QvRTCsEPvVCY","executionInfo":{"status":"ok","timestamp":1689989542710,"user_tz":420,"elapsed":6350,"user":{"displayName":"Yu Wang","userId":"02802289530208391889"}},"outputId":"d0b271db-9f13-4145-80cd-36e479fb848f"},"execution_count":2,"outputs":[{"output_type":"stream","name":"stdout","text":["Downloading...\n","From: https://drive.google.com/uc?id=1a1v06PvGaWmTCWvL33o2C133D90SRzMC\n","To: /content/cw_texts_clean_bert.csv\n","100% 59.5M/59.5M [00:00<00:00, 66.5MB/s]\n"]}]},{"cell_type":"code","source":["import pandas as pd\n","from sklearn.model_selection import train_test_split\n","from datasets import Dataset\n","from transformers import AutoTokenizer, AutoModelForSequenceClassification\n","from sklearn.metrics import mean_absolute_error\n","from transformers import TrainingArguments, Trainer\n","import torch\n","import matplotlib.pyplot as plt\n","from sklearn.metrics import mean_squared_error\n","from sklearn.metrics import r2_score\n","from numpy.random import seed\n","from tqdm import tqdm\n","seed(1)\n","\n","final = pd.read_csv('/data/cw_texts_clean_bert.csv')\n","\n","print(len(final))\n","final['fatalities_log'] = final['fatalities_log'].astype(float)\n","\n","train = final[final['year'] < 2021]\n","train.reset_index(inplace=True, drop=True)\n","test = final[final['year'] == 2021]\n","test.reset_index(inplace=True, drop=True)\n","\n","train['split_dummy'] = ''\n","train['year'] = train['year'].astype(int)\n","train['month'] = train['month'].astype(int)\n","\n","for i in tqdm(range(len(train))):\n","    if train['year'][i] == 2020 and train['month'][i] > 6:\n","        train['split_dummy'][i] = 1\n","    else:\n","        train['split_dummy'][i] = 0\n","\n","train['split_dummy'].value_counts()\n","\n","train1 = train[train['split_dummy'] == 0]\n","vali = train[train['split_dummy'] == 1]\n","\n","X_train = train1['final_text']\n","y_train = train1['fatalities_log']\n","X_vali = vali['final_text']\n","y_vali = vali['fatalities_log']\n","X_test = test['final_text']\n","y_test = test['fatalities_log']\n","X_train = pd.DataFrame(X_train)\n","X_test = pd.DataFrame(X_test)\n","X_vali = pd.DataFrame(X_vali)\n","y_vali = pd.DataFrame(y_vali)\n","y_train = pd.DataFrame(y_train)\n","y_test = pd.DataFrame(y_test)\n","\n","df_train = pd.concat([X_train, y_train], axis=1)\n","df_val = pd.concat([X_vali, y_vali], axis=1)\n","df_test = pd.concat([X_test, y_test], axis=1)\n","\n","\n","raw_train_ds = Dataset.from_pandas(df_train)\n","raw_val_ds = Dataset.from_pandas(df_val)\n","raw_test_ds = Dataset.from_pandas(df_test)\n","\n","BASE_MODEL = \"snowood1/ConfliBERT-scr-uncased\"\n","LEARNING_RATE = 2e-3\n","MAX_LENGTH = 256\n","BATCH_SIZE = 16\n","EPOCHS = 3\n","\n","tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL)\n","def model_init():\n","    model = AutoModelForSequenceClassification.from_pretrained(BASE_MODEL, num_labels=1, ignore_mismatched_sizes=True)\n","    for param in model.bert.parameters():\n","        param.requires_grad = False\n","    return model\n","\n","\n","ds = {\"train\": raw_train_ds, \"validation\": raw_val_ds, \"test\": raw_test_ds}\n","\n","\n","def preprocess_function(examples):\n","    label = examples[\"fatalities_log\"]\n","    examples = tokenizer(examples[\"final_text\"], truncation=True, padding=\"max_length\", max_length=256)\n","\n","    # Change this to real number\n","    examples[\"label\"] = float(label)\n","    return examples\n","\n","\n","for split in ds:\n","    ds[split] = ds[split].map(preprocess_function)\n","\n","\n","def compute_metrics_for_regression(eval_pred):\n","    logits, labels = eval_pred\n","    labels = labels.reshape(-1, 1)\n","\n","    mse = mean_squared_error(labels, logits)\n","    mae = mean_absolute_error(labels, logits)\n","    r2 = r2_score(labels, logits)\n","    single_squared_errors = ((logits - labels).flatten() ** 2).tolist()\n","\n","    # Compute accuracy\n","    # Based on the fact that the rounded score = true score only if |single_squared_errors| < 0.5\n","    accuracy = sum([1 for e in single_squared_errors if e < 0.25]) / len(single_squared_errors)\n","\n","    return {\"mse\": mse, \"mae\": mae, \"r2\": r2, \"accuracy\": accuracy}\n","\n","training_args = TrainingArguments(\n","    output_dir=\"/data/output/conflibert\",\n","    learning_rate=LEARNING_RATE,\n","    per_device_train_batch_size=BATCH_SIZE,\n","    per_device_eval_batch_size=BATCH_SIZE,\n","    num_train_epochs=EPOCHS,\n","    evaluation_strategy=\"steps\",\n","    eval_steps=500,\n","    save_total_limit=10,\n","    load_best_model_at_end=True,\n","    weight_decay=0.01,\n","    seed=123\n",")\n","\n","class RegressionTrainer(Trainer):\n","    def compute_loss(self, model, inputs, return_outputs=False):\n","        labels = inputs.pop(\"labels\")\n","        outputs = model(**inputs)\n","        logits = outputs[0][:, 0]\n","        loss = torch.nn.functional.mse_loss(logits, labels)\n","        return (loss, outputs) if return_outputs else loss\n","\n","\n","trainer = RegressionTrainer(\n","    model_init=model_init,\n","    args=training_args,\n","    train_dataset=ds[\"train\"],\n","    eval_dataset=ds[\"validation\"],\n","    compute_metrics=compute_metrics_for_regression,\n",")"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":804,"referenced_widgets":["883274d7fc4a4e2a9b8cde99a78956dc","4135200675a541dda8146d046b2d23c3","7f5da383869a489ba1ee57ed963a5553","9c5a102e2a3449be9611f3688ad30159","bb9a3dc1a11a4fafb94c27f094eefd61","5af4b11b207d46948030be6770d752de","1725346a610545118947e36024bf9a1f","8700775c2cb1442ca7393c5a714abd47","7b194ed803044d1b8eb0ea6299f38db3","cc882b1862bb4d10861716f81993ea98","37e3ada16f3746c8a3d8af0187a0a7e6","2ed65fb5913c434fa29f94faa5e85dc0","b2f3b087f56b49e8bc4ea3dc925e8cce","36bf8094f3a546dfb113fe268ac3f8cb","2b4d161611a5443cb5a224bdfebc084f","567ec1de920b4a8cba36856c2adf2f73","f8c2188d2ac54b48ad7518fe7114e09e","abad79cdec1644cdab7bd0afb510ab5d","a871fd50ff2f42358ac3c021e552a4ea","b561c159b4214e5bbf994b268cdd5000","7e695a68fef0404ba1d6b3e7357fbeb5","f3fb37119c334601b684c58cbdf05852","4db977a2ae2e4b8fb10eb680176677a0","6a3cc22a248f46fd92adc32e03018bc5","00a5d5ab4e124e64a83cdb916b3d05fc","e8d12ec700cb4743b15112f99f8b2a99","0fd790ee79764a6a86a7d9123b3d7d05","79ad6475dec84816b8f9b941f067f445","dfbcd7bbcb8c4b4a8c7e55b4937f5435","94522ada396f49a5a978a0f7fb7be84f","c54cf353e880481b8c3039bcd442b58d","a14e58325f9f4cf7a2d79f1f78deb8f5","c0d9656aedba4f55ba92ce7804ab113f","9c622c2fa2bf49338ef732763156ee3c","d40e27b7b50c455db43c441065b4c275","3527e248a7374e12947da43645c7744e","2607ce0fbe584cc7b6291f955daea4a7","a8c9b6f7799543c994ef1e6194c7a1aa","df5430a96f734c3a9b02101bc23aba1a","0539315729e64044b86c32118253940e","904b04b9f07142b8beea8c20e54a830e","da823d14ed1040eab8b2d141fcc26147","430cf190bc2242f0a4c38d585edc62ad","dc5e5f3634304a0097f1eb487f6da383","32da9ce8b6024293ba36613ea606188b","295696f17b224523b3b1966873924f71","36884bd369b14fe9b9e6f536fb2160ff","4b1c2cef15b64efc88f8316c724e669b","df436acbaca245899129bb4d78606a60","7514415d0cb1487d977e9c314935bf36","abc3fe3bd45b434abf31931b7b5f440d","6f0761dba1724f35ba1207f8ca423658","ea0743907abd4ac5b54ec9d3cbb800cb","29d5ab2d2e6344e1b237e4b81e34042f","0b40d9183b444507ab74d3553fcc529a","46ea5181a2ed446a9f4c50251b78a28d","46d9e5b70c7a4b17892f3cdb48b15559","a60edcbf6b57455eb10421081b619c5e","a79e1c3551cd430fa52bca3a933db45e","3c6c30732b994c388d5ce54595070042","00e31b85c0624518a742318b8bbc76da","262cd3a4288646138dfd190ec3c6052d","40369fb187994dc29155214875387521","6b3a7d5110d545a398ba347818e5a8f4","2fa16260dfa347deb7a2d40eec2b3ed8","4589da3f705d4e7b939d35748552145c","8f879869276842a58f7a9614bbf6b8b4","dc445b748d954d84a283de51c9dfd6af","9a31427a40f64762b623ba112623b69d","bcfb01e5a1a8415c83b5c6ac31af42ac","83b576a5ab7d4fa6b6cae342bdb023f1","82cc047e6b124b56b63f57bb68bd7ddb","282b3f64f75545208db084a6682db43f","d20027d45c874b8fb02be6341e7f52e1","8b3966021fea4f4e869023cbf87b0435","3a0333bf6a544af9b2b6dc1cc522b167","18e243713c79441d8d5e6d7de028a220","41ac01451b084b89b870b73b40b04ee1","42b06595d8f44a29b493b20c4e649c5f","ceb981e1af854a47bce725bb9d2071d2","456d42166d7745069683aa6e12cb29e3","08d3e04bb839421ab40d7e0d9164bfe2","8ce935b48cf049f9b83f4463cb33cbf9","f2c01d9008224b318d86b863c9f0af1c","f4f3bc499929474db1dc05e013b62f54","ac75074c28084041a24c878659f06015","48fbf924d41748b69e9e7cf372215bb4","dcde05fd0d634c589f29b123d99625e6","b7a322f72fd141dcae8709c48e03934a","72b9c7e6774c43c5aa2c387ec05f11fb","8328295a05364894bbd7b8b610e31182","cb96481768c94a61b36f3ce28b04ed08","2582c933f8234b0fae6c678912e38a6a","9a6cae14526e44e2bb42274f53912d97","7b0d84f37631419486fd8cce5eadd438","e018688f102942039b3129305a85e2f1","bf02113db7604b629112a99861b5d9fa","d244fb06e3dd4f4a9ab070c5308e66af","d2e89dcc8206485fb3ec1fb3d196edcf"]},"id":"_UUtlbRAySQ_","executionInfo":{"status":"ok","timestamp":1689989715573,"user_tz":420,"elapsed":91661,"user":{"displayName":"Yu Wang","userId":"02802289530208391889"}},"outputId":"ad631b64-ae4d-45fd-bf9b-f9d8e950998b"},"execution_count":3,"outputs":[{"output_type":"stream","name":"stderr","text":["<ipython-input-3-15567ef26342>:25: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  train['split_dummy'] = ''\n","<ipython-input-3-15567ef26342>:26: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  train['year'] = train['year'].astype(int)\n","<ipython-input-3-15567ef26342>:27: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame.\n","Try using .loc[row_indexer,col_indexer] = value instead\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  train['month'] = train['month'].astype(int)\n"]},{"output_type":"stream","name":"stdout","text":["23868\n"]},{"output_type":"stream","name":"stderr","text":["  0%|          | 0/22572 [00:00<?, ?it/s]<ipython-input-3-15567ef26342>:33: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  train['split_dummy'][i] = 0\n","<ipython-input-3-15567ef26342>:31: SettingWithCopyWarning: \n","A value is trying to be set on a copy of a slice from a DataFrame\n","\n","See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n","  train['split_dummy'][i] = 1\n","100%|██████████| 22572/22572 [00:06<00:00, 3439.49it/s]\n"]},{"output_type":"display_data","data":{"text/plain":["Downloading (…)okenizer_config.json:   0%|          | 0.00/270 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"883274d7fc4a4e2a9b8cde99a78956dc"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading (…)lve/main/config.json:   0%|          | 0.00/570 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"2ed65fb5913c434fa29f94faa5e85dc0"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading (…)solve/main/vocab.txt:   0%|          | 0.00/224k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"4db977a2ae2e4b8fb10eb680176677a0"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading (…)/main/tokenizer.json:   0%|          | 0.00/454k [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"9c622c2fa2bf49338ef732763156ee3c"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading (…)cial_tokens_map.json:   0%|          | 0.00/112 [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"32da9ce8b6024293ba36613ea606188b"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Map:   0%|          | 0/21924 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"46ea5181a2ed446a9f4c50251b78a28d"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Map:   0%|          | 0/648 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"8f879869276842a58f7a9614bbf6b8b4"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Map:   0%|          | 0/1296 [00:00<?, ? examples/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"41ac01451b084b89b870b73b40b04ee1"}},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["Downloading pytorch_model.bin:   0%|          | 0.00/437M [00:00<?, ?B/s]"],"application/vnd.jupyter.widget-view+json":{"version_major":2,"version_minor":0,"model_id":"b7a322f72fd141dcae8709c48e03934a"}},"metadata":{}},{"output_type":"stream","name":"stderr","text":["Some weights of the model checkpoint at snowood1/ConfliBERT-scr-uncased were not used when initializing BertForSequenceClassification: ['cls.predictions.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.decoder.weight', 'cls.predictions.decoder.bias']\n","- This IS expected if you are initializing BertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n","- This IS NOT expected if you are initializing BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n","Some weights of BertForSequenceClassification were not initialized from the model checkpoint at snowood1/ConfliBERT-scr-uncased and are newly initialized: ['classifier.bias', 'classifier.weight', 'bert.pooler.dense.bias', 'bert.pooler.dense.weight']\n","You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n"]}]},{"cell_type":"code","source":["print(trainer.model)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"rUtufi07OSX8","executionInfo":{"status":"ok","timestamp":1689990014374,"user_tz":420,"elapsed":415,"user":{"displayName":"Yu Wang","userId":"02802289530208391889"}},"outputId":"1df34c79-0214-41c7-fc8c-9f20ee8f1031"},"execution_count":9,"outputs":[{"output_type":"stream","name":"stdout","text":["BertForSequenceClassification(\n","  (bert): BertModel(\n","    (embeddings): BertEmbeddings(\n","      (word_embeddings): Embedding(30000, 768, padding_idx=0)\n","      (position_embeddings): Embedding(512, 768)\n","      (token_type_embeddings): Embedding(2, 768)\n","      (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","      (dropout): Dropout(p=0.1, inplace=False)\n","    )\n","    (encoder): BertEncoder(\n","      (layer): ModuleList(\n","        (0-11): 12 x BertLayer(\n","          (attention): BertAttention(\n","            (self): BertSelfAttention(\n","              (query): Linear(in_features=768, out_features=768, bias=True)\n","              (key): Linear(in_features=768, out_features=768, bias=True)\n","              (value): Linear(in_features=768, out_features=768, bias=True)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","            )\n","            (output): BertSelfOutput(\n","              (dense): Linear(in_features=768, out_features=768, bias=True)\n","              (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","              (dropout): Dropout(p=0.1, inplace=False)\n","            )\n","          )\n","          (intermediate): BertIntermediate(\n","            (dense): Linear(in_features=768, out_features=3072, bias=True)\n","            (intermediate_act_fn): GELUActivation()\n","          )\n","          (output): BertOutput(\n","            (dense): Linear(in_features=3072, out_features=768, bias=True)\n","            (LayerNorm): LayerNorm((768,), eps=1e-12, elementwise_affine=True)\n","            (dropout): Dropout(p=0.1, inplace=False)\n","          )\n","        )\n","      )\n","    )\n","    (pooler): BertPooler(\n","      (dense): Linear(in_features=768, out_features=768, bias=True)\n","      (activation): Tanh()\n","    )\n","  )\n","  (dropout): Dropout(p=0.1, inplace=False)\n","  (classifier): Linear(in_features=768, out_features=1, bias=True)\n",")\n"]}]},{"cell_type":"code","source":["print(sum(p.numel() for p in trainer.model.classifier.parameters()))\n","print(sum(p.numel() for p in trainer.model.bert.pooler.parameters()))\n","print(sum(p.numel() for p in trainer.model.bert.encoder.parameters()))\n","print(sum(p.numel() for p in trainer.model.bert.embeddings.parameters()))\n","print(sum(p.numel() for p in trainer.model.bert.embeddings.parameters()))\n","print(sum(p.numel() for p in trainer.model.dropout.parameters()))"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Fp3vCrorPVkV","executionInfo":{"status":"ok","timestamp":1689989891707,"user_tz":420,"elapsed":325,"user":{"displayName":"Yu Wang","userId":"02802289530208391889"}},"outputId":"8dda1bb4-0a5d-4c9a-b99a-ceea49ce82f2"},"execution_count":6,"outputs":[{"output_type":"stream","name":"stdout","text":["769\n","590592\n","85054464\n","23436288\n","23436288\n","0\n"]}]},{"cell_type":"code","source":["print(list(trainer.model.bert.pooler.parameters()))\n","print(list(trainer.model.bert.pooler.parameters())[0].shape)\n","print(list(trainer.model.bert.pooler.parameters())[1].shape)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"eRa2RVcZQYn5","executionInfo":{"status":"ok","timestamp":1689990179858,"user_tz":420,"elapsed":417,"user":{"displayName":"Yu Wang","userId":"02802289530208391889"}},"outputId":"94d4ed92-1e42-42ee-8554-60495071712f"},"execution_count":15,"outputs":[{"output_type":"stream","name":"stdout","text":["[Parameter containing:\n","tensor([[ 0.0023,  0.0292,  0.0027,  ..., -0.0195, -0.0074,  0.0457],\n","        [-0.0240,  0.0098,  0.0159,  ...,  0.0071,  0.0038, -0.0017],\n","        [-0.0469, -0.0091,  0.0459,  ..., -0.0254, -0.0225,  0.0293],\n","        ...,\n","        [-0.0263, -0.0083, -0.0108,  ..., -0.0193,  0.0212,  0.0289],\n","        [ 0.0192, -0.0194, -0.0059,  ..., -0.0121, -0.0184,  0.0126],\n","        [ 0.0188,  0.0111, -0.0081,  ..., -0.0005,  0.0238, -0.0071]],\n","       device='cuda:0'), Parameter containing:\n","tensor([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n","        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n","        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n","        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n","        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n","        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n","        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n","        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n","        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n","        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n","        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n","        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n","        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n","        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n","        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n","        0., 0., 0., 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768])\n","torch.Size([768])\n"]}]},{"cell_type":"code","source":["trainer.train()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":485},"id":"CX4GQadCPS1h","executionInfo":{"status":"ok","timestamp":1689990517476,"user_tz":420,"elapsed":301578,"user":{"displayName":"Yu Wang","userId":"02802289530208391889"}},"outputId":"18060a1f-af25-4f40-80d5-3074fb775ed0"},"execution_count":16,"outputs":[{"output_type":"stream","name":"stderr","text":["Some weights of the model checkpoint at snowood1/ConfliBERT-scr-uncased were not used when initializing BertForSequenceClassification: ['cls.predictions.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.LayerNorm.bias', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.weight', 'cls.predictions.decoder.weight', 'cls.predictions.decoder.bias']\n","- This IS expected if you are initializing BertForSequenceClassification from the checkpoint of a model trained on another task or with another architecture (e.g. initializing a BertForSequenceClassification model from a BertForPreTraining model).\n","- This IS NOT expected if you are initializing BertForSequenceClassification from the checkpoint of a model that you expect to be exactly identical (initializing a BertForSequenceClassification model from a BertForSequenceClassification model).\n","Some weights of BertForSequenceClassification were not initialized from the model checkpoint at snowood1/ConfliBERT-scr-uncased and are newly initialized: ['classifier.bias', 'classifier.weight', 'bert.pooler.dense.bias', 'bert.pooler.dense.weight']\n","You should probably TRAIN this model on a down-stream task to be able to use it for predictions and inference.\n","/usr/local/lib/python3.10/dist-packages/transformers/optimization.py:391: FutureWarning: This implementation of AdamW is deprecated and will be removed in a future version. Use the PyTorch implementation torch.optim.AdamW instead, or set `no_deprecation_warning=True` to disable this warning\n","  warnings.warn(\n"]},{"output_type":"display_data","data":{"text/plain":["<IPython.core.display.HTML object>"],"text/html":["\n","    <div>\n","      \n","      <progress value='4113' max='4113' style='width:300px; height:20px; vertical-align: middle;'></progress>\n","      [4113/4113 04:56, Epoch 3/3]\n","    </div>\n","    <table border=\"1\" class=\"dataframe\">\n","  <thead>\n"," <tr style=\"text-align: left;\">\n","      <th>Step</th>\n","      <th>Training Loss</th>\n","      <th>Validation Loss</th>\n","      <th>Mse</th>\n","      <th>Mae</th>\n","      <th>R2</th>\n","      <th>Accuracy</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <td>500</td>\n","      <td>1.852100</td>\n","      <td>2.098717</td>\n","      <td>2.098717</td>\n","      <td>1.088040</td>\n","      <td>0.464202</td>\n","      <td>0.211420</td>\n","    </tr>\n","    <tr>\n","      <td>1000</td>\n","      <td>1.658700</td>\n","      <td>1.955220</td>\n","      <td>1.955220</td>\n","      <td>0.962297</td>\n","      <td>0.500836</td>\n","      <td>0.487654</td>\n","    </tr>\n","    <tr>\n","      <td>1500</td>\n","      <td>1.615400</td>\n","      <td>1.959996</td>\n","      <td>1.959996</td>\n","      <td>0.950942</td>\n","      <td>0.499617</td>\n","      <td>0.470679</td>\n","    </tr>\n","    <tr>\n","      <td>2000</td>\n","      <td>1.594900</td>\n","      <td>1.912428</td>\n","      <td>1.912428</td>\n","      <td>0.956037</td>\n","      <td>0.511761</td>\n","      <td>0.435185</td>\n","    </tr>\n","    <tr>\n","      <td>2500</td>\n","      <td>1.586500</td>\n","      <td>1.881854</td>\n","      <td>1.881854</td>\n","      <td>0.936825</td>\n","      <td>0.519567</td>\n","      <td>0.513889</td>\n","    </tr>\n","    <tr>\n","      <td>3000</td>\n","      <td>1.599000</td>\n","      <td>1.810188</td>\n","      <td>1.810188</td>\n","      <td>0.893865</td>\n","      <td>0.537863</td>\n","      <td>0.543210</td>\n","    </tr>\n","    <tr>\n","      <td>3500</td>\n","      <td>1.639200</td>\n","      <td>1.777411</td>\n","      <td>1.777411</td>\n","      <td>0.867534</td>\n","      <td>0.546231</td>\n","      <td>0.540123</td>\n","    </tr>\n","    <tr>\n","      <td>4000</td>\n","      <td>1.490000</td>\n","      <td>1.802616</td>\n","      <td>1.802616</td>\n","      <td>0.869624</td>\n","      <td>0.539796</td>\n","      <td>0.512346</td>\n","    </tr>\n","  </tbody>\n","</table><p>"]},"metadata":{}},{"output_type":"execute_result","data":{"text/plain":["TrainOutput(global_step=4113, training_loss=1.6255456176431342, metrics={'train_runtime': 299.6766, 'train_samples_per_second': 219.477, 'train_steps_per_second': 13.725, 'total_flos': 8652592477734912.0, 'train_loss': 1.6255456176431342, 'epoch': 3.0})"]},"metadata":{},"execution_count":16}]},{"cell_type":"code","source":["# Note (see the log above): Some weights of BertForSequenceClassification were not initialized from the model checkpoint at snowood1/ConfliBERT-scr-uncased and are newly initialized: ['classifier.bias', 'classifier.weight', 'bert.pooler.dense.bias', 'bert.pooler.dense.weight'].\n","print(list(trainer.model.bert.pooler.parameters()))\n","print(list(trainer.model.bert.pooler.parameters())[0].shape)\n","print(list(trainer.model.bert.pooler.parameters())[1].shape)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"rUhxenWGRYGy","executionInfo":{"status":"ok","timestamp":1689990542149,"user_tz":420,"elapsed":391,"user":{"displayName":"Yu Wang","userId":"02802289530208391889"}},"outputId":"a2f922f1-ba58-49e8-fc9c-4bf18b01014b"},"execution_count":17,"outputs":[{"output_type":"stream","name":"stdout","text":["[Parameter containing:\n","tensor([[ 0.0023,  0.0292,  0.0027,  ..., -0.0195, -0.0074,  0.0457],\n","        [-0.0240,  0.0098,  0.0159,  ...,  0.0071,  0.0038, -0.0017],\n","        [-0.0469, -0.0091,  0.0459,  ..., -0.0254, -0.0225,  0.0293],\n","        ...,\n","        [-0.0263, -0.0083, -0.0108,  ..., -0.0193,  0.0212,  0.0289],\n","        [ 0.0192, -0.0194, -0.0059,  ..., -0.0121, -0.0184,  0.0126],\n","        [ 0.0188,  0.0111, -0.0081,  ..., -0.0005,  0.0238, -0.0071]],\n","       device='cuda:0'), Parameter containing:\n","tensor([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n","        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n","        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n","        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n","        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n","        0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n","        0., 0., 0., 0., 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